Guardian

Goals

Desktop workspace where chat, terminal, notes, and memory live in one local-first interface. You pick the model, the data stays on your machine.

Process

Started as an Electron shell with a chat window and a local Ollama connection. Built outward from there.

Routing. Provider-agnostic engine across Anthropic, OpenAI, and Ollama. Auto-selects by intent complexity and cost. Runs air-gapped on local models or through cloud APIs.

Reframe detection. Classifies when model responses subtly reshape user statements across 7 types. Triggers prompt correction when user-rated inaccuracy exceeds 40%.

Awareness-trap detection. Separate from reframe detection — identifies recurring unresolved patterns in conversations over time. Schema, accessors, and pipeline integration wired into the post-session flow. This is the Guardian layer that watches for what you keep circling back to without resolving.

Memory. 4-level compression pipeline (raw -> summary -> pattern -> principle). Strength decays over time, reinforces on retrieval.

Sovereign encryption. Notes support sensitivity levels and context gating. Encrypted at rest with IPC-based unlock/lock cycle. Private notes stay private — they don’t flow into cloud context even when cloud providers are active.

ForgeFrame fusion. JSON-RPC stdio client bridges Guardian to ForgeFrame’s MCP memory server. Sessions sync bidirectionally. Data migration pipeline handles the transition from Guardian’s internal SQLite to ForgeFrame’s shared memory layer.

Post-session pipeline. Fires on conversation end. Extracts decisions, tasks, and code artifacts. Generates typed notes. Indexes into FTS5 for search. Links entities into a knowledge graph. Runs awareness-trap detection.

Limitations

• Reframe detection uses heuristic classification, not a trained model. False positive rate unmeasured.
• Memory compression thresholds are hand-tuned, not empirically optimized.
• No formal user study on reframe detection or awareness-trap accuracy.
• Sovereign encryption is functional but not audited by a third party.
• v1.0 gap closing is in progress — tab disclosure and weekly synthesis still being finalized.

Learnings

The hardest problems were state synchronization and process lifecycle management, not the models themselves.

Reframe detection surfaced a deeper problem: models subtly reshape your language over time. Detecting that requires understanding the user’s baseline, which requires memory, which requires local persistence. That dependency chain is why ForgeFrame exists.

Awareness-trap detection surfaced something else: the patterns you avoid aren’t in individual conversations — they’re in the gaps between them. You need memory that spans sessions to see what you keep not talking about.